Optimization of prototype and machine design within a 3d fluid modeling environment

ABSTRACT

Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

CROSS-REFERENCE

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/516,099, filed Jun. 6, 2017, and entitled “A MULTIPLE FLUID MODELTOOL FOR INTERDISCIPLINARY FLUID MODELING”, and U.S. Provisional PatentApplication No. 62/469,953, filed Mar. 10, 2017, and entitled “AMULTIPLE FLUID MODEL TOOL FOR INTERDISCIPLINARY FLUID MODELING”. Theentireties of the foregoing applications are hereby incorporated byreference herein.

TECHNICAL FIELD

This disclosure relates generally to three dimensional modeling systems,and more specifically, to modeling of a fluid system and/or a fluidsystem design tool.

BACKGROUND

During a design phase of a device or product associated with a fluidsystem, it is often desirable to determine impact of a fluid withrespect to the device or product associated with the fluid system. Todetermine impact of the fluid with respect to the design, numericalanalysis of two dimensional (2D) data associated with computationalfluid dynamics can be employed to analyze fluid flow through the deviceor product. For instance, a color of a 2D surface associated with thedevice or product can represent a degree of fluid flow. However,analyzing impact of a fluid with respect to a design of the device orproduct generally involves human interpretation of 2D data, which canresult in human trial and error with respect to the fluid system.Moreover, human interpretation of 2D data and/or employing multiplefluid model tools to determine impact of a fluid with respect to adesign of a device or product can be burdensome with respect to cost,redundancy and/or maintenance associated with the device or product.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification, nor delineate any scope of the particularimplementations of the specification or any scope of the claims. Itssole purpose is to present some concepts of the specification in asimplified form as a prelude to the more detailed description that ispresented later.

In accordance with an embodiment, a system includes a modelingcomponent, a machine learning component, and a three-dimensional designcomponent. The modeling component generates a three-dimensional model ofa mechanical device based on a library of stored data elements. Themachine learning component predicts one or more characteristics of themechanical device based on a first machine learning process associatedwith the three-dimensional model. The machine learning component alsogenerates physics modeling data of the mechanical device based on theone or more characteristics of the mechanical device. The graphical userinterface component provides, via a graphical user interface, athree-dimensional design environment associated with thethree-dimensional model and a probabilistic simulation environmentassociated with optimization of the three-dimensional model. Thethree-dimensional design environment renders the physics modeling dataon the three-dimensional model. The probabilistic simulation environmentrenders a modified version of the physics modeling data on thethree-dimensional model based on a second machine learning processassociated with the optimization of the three-dimensional model.

In accordance with another embodiment, a method provides for generating,by a system comprising a processor, a three-dimensional model of amechanical device based on a library of stored data elements. The methodalso provides for performing, by the system, a first machine learningprocess associated with the three-dimensional model to predict one ormore characteristics of the mechanical device. Furthermore, the methodprovides for generating, by the system, physics modeling data of themechanical device based on the one or more characteristics of themechanical device. The method also provides for generating, by thesystem, a graphical user interface that presents a three-dimensionaldesign environment associated with the three-dimensional model and aprobabilistic simulation environment associated with optimization of thethree-dimensional model, comprising rendering the physics modeling dataon the three-dimensional model via the three-dimensional designenvironment, and rendering a modified version of the physics modelingdata on the three-dimensional model via the probabilistic simulationenvironment based on a second machine learning process associated withthe optimization of the three-dimensional model.

In accordance with yet another embodiment, a computer readable storagedevice comprising instructions that, in response to execution, cause asystem comprising a processor to perform operations, comprising:generating a three-dimensional model of a mechanical device based on alibrary of stored data elements, performing a machine learning processassociated with the three-dimensional model to predict one or morecharacteristics of the mechanical device, determining physics modelingdata of the mechanical device based on the one or more characteristicsof the mechanical device, and providing a graphical user interface thatpresents a three-dimensional design environment associated with thethree-dimensional model and a probabilistic simulation environmentassociated with optimization of the three-dimensional model.

The following description and the annexed drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the following detaileddescription of the specification when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee. Numerous aspects, implementations, objects andadvantages of the present invention will be apparent upon considerationof the following detailed description, taken in conjunction with theaccompanying drawings, in which like reference characters refer to likeparts throughout, and in which:

FIG. 1 illustrates a high-level block diagram of an example fluid modeltool component, in accordance with various aspects and implementationsdescribed herein;

FIG. 2 illustrates a high-level block diagram of another example fluidmodel tool component, in accordance with various aspects andimplementations described herein;

FIG. 3 illustrates a high-level block diagram of yet another examplefluid model tool component, in accordance with various aspects andimplementations described herein;

FIG. 4 illustrates a high-level block diagram of an example fluid modeltool component in communication with a user display device, inaccordance with various aspects and implementations described herein;

FIG. 5 illustrates an example system that facilitates optimization ofprototype and machine design within a three dimensional fluid modelingenvironment, in accordance with various aspects and implementationsdescribed herein;

FIG. 6 illustrates an example graphical user interface, in accordancewith various aspects and implementations described herein;

FIG. 7 illustrates an example 3D model, in accordance with variousaspects and implementations described herein;

FIG. 8 illustrates another example 3D model, in accordance with variousaspects and implementations described herein;

FIG. 9 illustrates yet another example 3D model, in accordance withvarious aspects and implementations described herein;

FIG. 10 illustrates yet another example 3D model, in accordance withvarious aspects and implementations described herein;

FIG. 11 depicts a flow diagram of an example method for providinginterdisciplinary fluid modeling, in accordance with various aspects andimplementations described herein;

FIG. 12 depicts a flow diagram of another example method for providinginterdisciplinary fluid modeling, in accordance with various aspects andimplementations described herein;

FIG. 13 is a schematic block diagram illustrating a suitable operatingenvironment; and

FIG. 14 is a schematic block diagram of a sample-computing environment.

DETAILED DESCRIPTION

Various aspects of this disclosure are now described with reference tothe drawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It should beunderstood, however, that certain aspects of this disclosure may bepracticed without these specific details, or with other methods,components, materials, etc. In other instances, well-known structuresand devices are shown in block diagram form to facilitate describing oneor more aspects.

Systems and techniques that facilitate optimization of prototype andmachine design within a three dimensional (3D) fluid modelingenvironment are presented. For example, as compared to conventionalanalysis of a fluid system that involves human interpretation oftwo-dimensional (2D) data and/or human trial and error with respect to afluid system, the subject innovations provide for a three-dimensional(3D) design environment and/or a probabilistic simulation environmentthat can facilitate optimization of prototype and machine design. In anaspect, physics modeling data associated with a degree of fluid flow canbe rendered on a 3D model of a device and optimized to facilitateoptimization of a design for the device. In one example, visualcharacteristics of the physics modeling data can be dynamic based on thedegree of fluid flow and/or optimization of the physics modeling data.Various systems and techniques disclosed herein can be related tocloud-based services, a heating, ventilation and air conditioning (HVAC)system, a medical system, an automobile, an aircraft, a water craft, awater filtration system, a cooling system, pumps, engines, diagnostics,prognostics, optimized machine design factoring in cost of materials inreal-time, explicit and/or implicit training of models through real-timeaggregation of data, etc. In an embodiment, a graphical user interfacecan provide a platform for interdisciplinary fluid modeling andoptimization of the fluid modeling. For instance, a graphical userinterface can allow for optimization of prototype and machine designwith respect to fluid dynamics, thermal characteristics (e.g.,thermodynamics) and/or combustion. The optimization of prototype andmachine design can be provided within a 3D modeling environment. In animplementation, a 3D model can be utilized in connection with selectingideal components for a machine based on characteristics of thecomponents such as, for example, material (e.g., metal, alloy, etc.),shape, dimension, thermal characteristics, expansion characteristicsand/or other characteristics in order to optimize the machine designand/or achieve a best cost-benefit design for the machine. An embodimentcan also provide for rapid prototyping of the machine via the 3Dmodeling environment. As such, a 3D model associated with physicsmodeling can be generated more efficiently and/or data provided by a 3Dmodel associated with physics modeling can be more accurate. Moreover,damage to a device, machine and/or component associated with a 3D modelcan be minimized by replacing human trial and error for analyzing one ormore characteristics associated with the 3D model of the device, machineand/or component.

Referring initially to FIG. 1, there is illustrated an example system100 that provides a multiple fluid model tool for interdisciplinaryfluid modeling, according to an aspect of the subject disclosure. Thesystem 100 can be employed by various systems, such as, but not limitedto modeling systems, aviation systems, power systems, distributed powersystems, energy management systems, thermal management systems,transportation systems, oil and gas systems, mechanical systems, machinesystems, device systems, cloud-based systems, heating systems, HVACsystems, medical systems, automobile systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems,engine systems, diagnostics systems, prognostics systems, machine designsystems, medical device systems, medical imaging systems, medicalmodeling systems, simulation systems, enterprise systems, enterpriseimaging solution systems, advanced diagnostic tool systems, imagemanagement platform systems, artificial intelligence systems, machinelearning systems, neural network systems, and the like. In one example,the system 100 can be associated with a graphical user interface systemto facilitate visualization and/or interpretation of 3D data. Moreover,the system 100 and/or the components of the system 100 can be employedto use hardware and/or software to solve problems that are highlytechnical in nature (e.g., related to processing 3D data, related tomodeling 3D data, related to artificial intelligence, etc.), that arenot abstract and that cannot be performed as a set of mental acts by ahuman.

The system 100 can include a fluid model tool component 102 that caninclude a modeling component 104, a machine learning component 106and/or a graphical user interface (GUI) component 107. In an embodiment,the GUI component 107 can include a 3D design component 108 and/or aprobabilistic simulation component 109. In an aspect, modeling performedby the fluid model tool component 102 can be associated with a flowintegrated design environment, a heat transfer design environment and/ora combustion design environment. Aspects of the systems, apparatuses orprocesses explained in this disclosure can constitute machine-executablecomponent(s) embodied within machine(s), e.g., embodied in one or morecomputer readable mediums (or media) associated with one or moremachines. Such component(s), when executed by the one or more machines,e.g., computer(s), computing device(s), virtual machine(s), etc. cancause the machine(s) to perform the operations described. The system 100(e.g., the fluid model tool component 102) can include memory 110 forstoring computer executable components and instructions. The system 100(e.g., the fluid model tool component 102) can further include aprocessor 112 to facilitate operation of the instructions (e.g.,computer executable components and instructions) by the system 100(e.g., the fluid model tool component 102). In certain embodiments, thesystem 100 can further include a library of data elements 114. Thelibrary of data elements 114 can be a library of stored data elements.

The modeling component 104 can generate a 3D model of a device. The 3Dmodel can be a 3D representation of the device for presentation via a 3Ddesign environment. In an embodiment, the modeling component 104 cangenerate a 3D model of a mechanical device and/or an electronic device.The modeling component 104 can generate a 3D model of a device based on,for example, the library of data elements 114. The library of dataelements 114 can include a set of data elements for mechanicalcomponents and/or electrical components. Furthermore, the set of dataelements can include, for example, geometry data and/or texture data.The geometry data can be indicative of a geometry of the device. In anaspect, the geometry data can include 3D data points (e.g., 3D vertices)that form a shape, a structure and/or a set of surfaces of the devicevia a 3D coordinate system. The geometry data can also include a set ofpolygons (e.g., a set of geometric faces) based on the 3D data points.In an embodiment, the geometry data can include mesh data associatedwith the 3D data points and/or the set of polygons. In anotherembodiment, the geometry data can include non-uniform rational basisspline (NURBS) data. The NURBS data can include NURBS surface data thatrepresents a surface and/or a geometric shape of the 3D model based on aset of parameters that map surfaces in 3D coordinate system. The NURBSdata can also include a set of control points that form a shape of asurface associated with the NURBS surface data. In an non-limitingexample, the library of data elements 114 can include a data element forfluid source, a fuel source, flow elements, pipe systems, sealingsystems, pressure drop components (e.g., orifices, valves, fittings,junctions, transitions, etc.), diffusers, heat exchangers, controllers,pumps, fans, compressors, cavities, vortexes and/or other components.Additionally or alternatively, the library of data elements 114 caninclude experimental data (e.g., experimental test data) associated withthe device. For example, the library of data elements 114 can includeone or more properties of the device that is determined via one or moreexperiments and/or one or more research processes. The one or moreexperiments and/or one or more research processes can includedetermining and/or capturing the one or more properties via a physicalrepresentation of the device associated with the 3D model. The one ormore properties of the device can include, for example, one or morephysical properties of the device, one or more mechanical properties ofthe device, one or more measurements of the device, one or more materialproperties of the device, one or more electrical properties of thedevice, one or more thermal properties of the device and/or one or moreother properties of the device.

In certain embodiments, the modeling component 104 can perform modelingof one or more mechanical elements of a device (e.g., a mechanicaldevice and/or an electronic device). For example, the modeling component104 can determine a set of boundaries for features of mechanicalelements of the device. Furthermore, the modeling component 104 candetermine a set of physical characteristics for mechanical elements. Ina non-limiting example, the modeling component 104 can determine one ormore chambers of a device. The modeling component 104 can, for example,determine a set of boundaries that define the one or more chambers. Themodeling component 104 can also determine a set of physicalcharacteristics for the one or more chambers such as, for example, asize for the one or more chambers, a shape for the one or more chambers,a volume of the one or more chambers and/or another physicalcharacteristic for the one or more chambers. In an aspect, the modelingcomponent 104 can compute the one or more mechanical elements of thedevice based on the library of data elements 114. To compute the one ormore mechanical elements, the modeling component 104 can employ one ormore modeling techniques using the library of data elements 114. Assuch, the one or more mechanical elements can be one or morecomputationally derived elements. In another aspect, the modelingcomponent 104 can perform a modeling process associated with the one ormore modeling techniques to facilitate design of a system associatedwith the device, where the system includes a set of mechanical elementsthat are combined to form the device.

In an embodiment, the modeling component 104 can determine a set ofcontrol volumes associated with the device. For instance, the modelingcomponent 104 can overlay a set of control volumes on the device. Acontrol volume can be an abstraction of a region of the device throughwhich a fluid (e.g., a liquid or a gas) and/or an electrical currentflows. In one example, a control volume can correspond to a chamber ofthe device. The modeling component 104 can determine geometric featuresof the set of control volumes. For instance, the modeling component 104can determine computational control volumes (e.g., chambers) and/orgeometrical features of the computational control volumes. Controlvolumes can be connected via various types of preconfigured elementsand/or preconfigured components to construct an analysis computationalmodel that extends from supply to sink conditions. Control volumes canalso simulate run conditions for the preconfigured elements, thepreconfigured components and/or a system associated with the 3D model.The preconfigured elements and/or the preconfigured components can beincluded in the library of data elements 114, for example. For instance,the library of data elements 114 can include an extended library ofpreconfigured elements and/or preconfigured components that can beemployed by the modeling component 104 to facilitate modeling and/orsimulating a wide-range of physical phenomena includingcompressible/incompressible fluid flow, buoyancy driven flow, rotatingcavity system flow, conduction/convection/radiation heat transfer,combustion equilibrium-chemistry, species transport, etc. Physicalformulation of the preconfigured elements and/or the preconfiguredcomponents can be varied based on complexity of a physical phenomena tobe simulated. In an aspect, physical formulation of the preconfiguredelements and/or the preconfigured components can categorized asmachine-learning based elements (e.g., seals, leakages, compressors,fans, junctions, bends, valves, orifices, pipes, etc.). Additionally oralternatively, physical formulation of the preconfigured elements and/orthe preconfigured components can be categorized as computationallyderived based elements (e.g., modeling methods utilizing a combinationof analytical modeling techniques and experimental test data).Combination of the preconfigured elements and/or the preconfiguredcomponents can be employed by the modeling component 104 to constructthe 3D model that can be further employed (e.g., by the machine learningcomponent 106) to simulate and/or predict a machine steady state ortransient response.

In another embodiment, the modeling component 104 can employ 3Dcomputer-aided design (CAD) data to automatically create computationaldomains and/or control volumes (e.g., chambers/elements/components) forthe 3D model that can be employed (e.g., by the machine learningcomponent 106) to generate predictions for simulated machine conditionsfor a device associated with the 3D model. Automation of thecomputational model creation can significantly reduce the cycle time ofanalysis setup. Furthermore, computational domains can bebi-directionally linked to 3D CAD through geometric tags, CAD curvesparametric expressions, surfaces parametric tags, etc. For examplecomputational domains can be automatically updated when the CAD data isupdated. In yet another embodiment, the modeling component 104 canintegrate sub-components of a device (e.g., a mechanical device and/oran electronic device) and/or sub-models of a device (e.g., a mechanicaldevice and/or an electronic device) to form, for example,sub-combinations and/or models of an entire machine. In an aspect, themodeling component 104 can integrate a first flow network of a firstsub-component with a second flow network of a second sub-component.Additionally or alternatively, the modeling component 104 can integratefirst heat transfer throughout a first sub-component with second heattransfer throughout a second sub-component. Additionally oralternatively, the modeling component 104 can integrate first multiphaseflow through a first sub-component with second multiphase flow through asecond sub-component.

The machine learning component 106 can perform learning (e.g., explicitlearning and/or implicit learning) and/or can generate inferences withrespect to one or more 3D models generated by the modeling component104. The learning and/or generated inferences by the machine learningcomponent 106 can facilitate determination of one or morecharacteristics associated with the one or more 3D models generated bythe modeling component 104. The learning and/or generated inferences canbe determined by the machine learning component 106 via one or moremachine learning processes associated with the one or more 3D models.The one or more characteristics determined by the machine learningcomponent 106 can include, for example, one or more fluidcharacteristics associated with the one or more 3D models generated bythe modeling component 104, one or more thermal characteristicsassociated with the one or more 3D models generated by the modelingcomponent 104, one or more combustion characteristics associated withthe one or more 3D models generated by the modeling component 104, oneor more electrical characteristics associated with the one or more 3Dmodels generated by the modeling component 104 and/or one or more othercharacteristics associated with the one or more 3D models generated bythe modeling component 104. In an aspect, the machine learning component106 can predict and/or model a flow network of a mechanical elementassociated with the one or more 3D models, heat transfer throughout amechanical element associated with the one or more 3D models, combustionassociated with a mechanical element associated with the one or more 3Dmodels, multiphase flow through a mechanical element associated with theone or more 3D models and/or other characteristics of a mechanicalelement associated with the one or more 3D models.

In an embodiment, the machine learning component 106 can predict the oneor more characteristics associated with the one or more 3D models basedon input data and one or more machine learning processes associated withthe one or more 3D models. The input data can be, for example, a set ofparameters for a fluid capable of flowing through the one or more 3Dmodels, a set of parameters for a thermal energy capable of flowingthrough the one or more 3D models, a set of parameters for a combustionchemical reaction capable of flowing through the one or more 3D models,a set of parameters for electricity flowing through the one or more 3Dmodels, and/or another set of parameters for input provided to the oneor more 3D models. The one or more characteristics associated with theone or more 3D models can correspond to one or more characteristics ofthe device (e.g., the mechanical device and/or the electronic device).In one example, distinct types of control volumes (e.g., chambers)simulating reservoirs, volume mixing dynamics, volume inertial dynamics,volume pumping dynamics, and/or volume gravitational dynamics can beemployed by the machine learning component 106 to model and/or simulatevarious fluid flow conditions associated with the one or more 3D models.In an aspect, the machine learning component 106 can also employmeasured data and/or streamed data to set boundary conditions for one ormore machine learning processes. For example, the machine learningcomponent 106 can also employ measured data and/or streamed data to setboundary conditions for supply chambers and sink chambers and/or toestablish driving forces for simulated physics phenomena (e.g., fluiddynamics, thermal dynamics, combustion dynamics, angular momentum,etc.).

Additionally or alternatively, the machine learning component 106 canperform a probabilistic based utility analysis that weighs costs andbenefits related to the one or more 3D models generated by the modelingcomponent 104. The machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can also employ an automatic classification system and/or anautomatic classification process to facilitate learning and/orgenerating inferences with respect to the one or more 3D modelsgenerated by the modeling component 104. For example, the machinelearning component 106 (e.g., one or more machine learning processesperformed by the machine learning component 106) can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to learn and/or generate inferenceswith respect to the one or more 3D models generated by the modelingcomponent 104. The machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can employ, for example, a support vector machine (SVM) classifierto learn and/or generate inferences with respect to the one or more 3Dmodels generated by the modeling component 104. Additionally oralternatively, the machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can employ other classification techniques associated with Bayesiannetworks, decision trees and/or probabilistic classification models.Classifiers employed by the machine learning component 106 (e.g., one ormore machine learning processes performed by the machine learningcomponent 106) can be explicitly trained (e.g., via a generic trainingdata) as well as implicitly trained (e.g., via receiving extrinsicinformation). For example, with respect to SVM's that are wellunderstood, SVM's are configured via a learning or training phase withina classifier constructor and feature selection module. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, xn), toa confidence that the input belongs to a class—that is,f(x)=confidence(class).

In an aspect, the machine learning component 106 can include aninference component that can further enhance automated aspects of themachine learning component 106 utilizing in part inference based schemesto facilitate learning and/or generating inferences with respect to theone or more 3D models generated by the modeling component 104. Themachine learning component 106 (e.g., one or more machine learningprocesses performed by the machine learning component 106) can employany suitable machine-learning based techniques, statistical-basedtechniques and/or probabilistic-based techniques. For example, themachine learning component 106 (e.g., one or more machine learningprocesses performed by the machine learning component 106) can employexpert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedysearch algorithms, rule-based systems, Bayesian models (e.g., Bayesiannetworks), neural networks, other non-linear training techniques, datafusion, utility-based analytical systems, systems employing Bayesianmodels, etc. In another aspect, the machine learning component 106(e.g., one or more machine learning processes performed by the machinelearning component 106) can perform a set of machine learningcomputations associated with the one or more 3D models generated by themodeling component 104. For example, the machine learning component 106(e.g., one or more machine learning processes performed by the machinelearning component 106) can perform a set of clustering machine learningcomputations, a set of decision tree machine learning computations, aset of instance-based machine learning computations, a set of regressionmachine learning computations, a set of regularization machine learningcomputations, a set of rule learning machine learning computations, aset of Bayesian machine learning computations, a set of deep Boltzmannmachine computations, a set of deep belief network computations, a setof convolution neural network computations, a set of stackedauto-encoder computations and/or a set of different machine learningcomputations.

In an embodiment, the machine learning component 106 can predict fluidflow and physics behavior associated with the 3D model. For instance,the machine learning component 106 can perform a machine learningprocess associated with fluid flow through the 3D model. The machinelearning component 106 can perform the machine learning process based oninput data indicative of input received by a device associated with the3D model. For example, the input data can include fluid data indicativeof a fluid provided to a device associated with the 3D model. The fluiddata can include one or more properties of the fluid such as, forexample, a fluid type of the fluid, a density of the fluid, a viscosityof the fluid, a volume of the fluid, a weight of the fluid, atemperature of the fluid and/or another property of the fluid. The inputdata can by employed by the machine learning component 106 to predictthe fluid flow. The fluid flow can be, for example, fluid flow of theinput data (e.g., the fluid) through the device associated with the 3Dmodel. The physics behavior can be physics behavior of the fluid flow.For instance, the physics behavior can be simulated physics and/orchanges of the fluid flow. Furthermore, the physics behavior can besimulated fluid flow conditions associated with the 3D model. Thephysics behavior can also include correlations and/or behaviordetermined based on one or more mathematical equations associated withfluid flow such as, for example, conservation equations for massassociated with a fluid, conservation equations for momentum associatedwith a fluid, conservation equations for energy associated with a fluid,conservation equations for angular momentum associated with a fluid,and/or another mathematical equation associated with fluid flow.

Additionally or alternatively, the machine learning component 106 canpredict thermal characteristics and physics behavior associated with the3D model. For instance, the machine learning component 106 can perform amachine learning process associated with thermal characteristicsassociated with the 3D model. The machine learning component 106 canperform the machine learning process based on input data indicative ofinput received by a device associated with the 3D model. For example,the input data can include the fluid data indicative of a fluid providedto a device associated with the 3D model. Additionally or alternatively,the input data can include electrical data indicative of a voltageand/or a current provided to a device associated with the 3D model. Theinput data can by employed by the machine learning component 106 topredict the thermal characteristics. The thermal characteristics can be,for example, a temperature associated with one or more regions of the 3Dmodel, a heat capacity associated with one or more regions of the 3Dmodel, thermal expansion associated with one or more regions of the 3Dmodel, thermal conductivity associated with one or more regions of the3D model, thermal stress associated with one or more regions of the 3Dmodel, and/or another thermal characteristics associated with one ormore regions of the 3D model. The physics behavior can be physicsbehavior of the thermal characteristics. For instance, the physicsbehavior can be simulated physics and/or changes of the thermalcharacteristics. Furthermore, the physics behavior can be simulatedthermal conditions associated with the 3D model. The physics behaviorcan also include correlations and/or behavior determined based on one ormore mathematical equations associated with thermal characteristics suchas, for example, conservation equations for mass associated with thermalcharacteristics, conservation equations for momentum associated withthermal characteristics, conservation equations for energy associatedwith thermal characteristics, conservation equations for angularmomentum associated with thermal characteristics, and/or anothermathematical equation associated with thermal characteristics.

Additionally or alternatively, the machine learning component 106 canpredict combustion characteristics and physics behavior associated withthe 3D model. For instance, the machine learning component 106 canperform a machine learning process associated with combustioncharacteristics associated with the 3D model. The machine learningcomponent 106 can perform the machine learning process based on inputdata indicative of input received by a device associated with the 3Dmodel. For example, the input data can include the fluid data indicativeof a fluid provided to a device associated with the 3D model.Additionally or alternatively, the input data can include electricaldata indicative of a voltage and/or a current provided to a deviceassociated with the 3D model. Additionally or alternatively, the inputdata can include chemical data indicative of a chemical element providedto a device associated with the 3D model. The input data can by employedby the machine learning component 106 to predict the combustioncharacteristics. The combustion characteristics can be, for example,information related to a chemical reaction associated with one or moreregions of the 3D model such as, for example, a temperature measurement,a heating value, an elemental composition, a moisture content, adensity, an acoustic measurement and/or another combustioncharacteristic associated with one or more regions of the 3D model. Thephysics behavior can be physics behavior of the combustioncharacteristics. For instance, the physics behavior can be simulatedphysics and/or changes of the combustion characteristics. Furthermore,the physics behavior can be simulated combustion conditions associatedwith the 3D model. The physics behavior can also include correlationsand/or behavior determined based on one or more mathematical equationsassociated with combustion characteristics such as, for example,conservation equations for mass associated with combustioncharacteristics, conservation equations for momentum associated withcombustion characteristics, conservation equations for energy associatedwith combustion characteristics, conservation equations for angularmomentum associated with combustion characteristics, and/or anothermathematical equation associated with combustion characteristics.

In an embodiment, the modeling component 104 can integrate a first 3Dmodel associated with a first device (e.g., a first mechanical deviceand/or a first electronic device) and a second 3D model associated witha second device (e.g., a second mechanical device and/or a secondelectronic device) to generate a 3D model for a device. For example, a3D model generated by the modeling component 104 can be a combination oftwo or more 3D models. In an aspect, first geometric features of thefirst 3D model can be combined with second geometric features of thesecond 3D model. The first geometric features of the first 3D model caninclude, for example, chambers, cavities, channels, and/or othergeometric features of the first 3D model. Similarly, the secondgeometric features of the second 3D model can include, for example,chambers, cavities, channels, and/or other geometric features of thesecond 3D model. As such, chambers, cavities, channels, and/or othergeometric features of the first 3D model and the second 3D model can becombined. In another embodiment, the first 3D model can comprise a firstset of supply nodes and a first set of sink nodes that form a first flownetwork for characteristics of the first 3D model. For instance, fluidprovided through the first 3D model can flow from a supply node to asink node of the first 3D model. Additionally, the second 3D model cancomprise a second set of supply nodes and a second set of sink nodesthat form a second flow network for characteristics of the second 3Dmodel. For instance, fluid provided through the second 3D model can flowfrom a supply node to a sink node of the second 3D model. The modelingcomponent 104 can combine the first flow network of the first 3D modelwith the second flow network of the second 3D model. For example, thefirst set of supply nodes of the first 3D model can be combined with thesecond set of supply nodes of the second 3D model. Furthermore, thefirst set of sink nodes of the first 3D model can be combined with thesecond set of sink nodes of the second 3D model.

In another embodiment, the machine learning component 106 can perform afirst machine learning process associated with the first 3D model and asecond machine learning process associated with the second 3D model. Forinstance, the machine learning component 106 can perform learning (e.g.,explicit learning and/or implicit learning) and/or can generateinferences with respect to the first 3D model via the first machinelearning process. Furthermore, the machine learning component 106 canperform learning (e.g., explicit learning and/or implicit learning)and/or can generate inferences with respect to the second 3D model viathe second machine learning process. The learning and/or generatedinferences by the machine learning component 106 can facilitatedetermination of one or more characteristics associated with the one ormore 3D models generated by the modeling component 104. Furthermore, thelearning and/or generated inferences can be determined by the machinelearning component 106 via one or more machine learning processesassociated with the one or more 3D models. In an aspect, the machinelearning component 106 can predict one or more characteristics of thedevice based on the one or more first characteristics associated withthe first 3D model and the one or more second characteristics associatedwith the second 3D model. In one example, the machine learning component106 can predict the one or more characteristics of the device based onthe one or more first characteristics and the one or more secondcharacteristics. The one or more first characteristics can include firstfluid flow characteristics associated with the first 3D model, firstthermal characteristics associated with the first 3D model, firstcombustion characteristics associated with the first 3D model and/orfirst physics behavior characteristics associated with the first 3Dmodel. Furthermore, one or more second characteristics can includesecond fluid flow characteristics associated with the second 3D model,second thermal characteristics associated with the second 3D model,second combustion characteristics associated with the second 3D modeland/or second physics behavior characteristics associated with thesecond 3D model. In an embodiment, the machine learning component 106can facilitate interaction between the first 3D model and the second 3Dmodel based on the input data associated with the machine learningcomponent 106. For example, interaction of the one or more firstcharacteristics associated with the first 3D model and the one or moresecond characteristics associated with the second 3D model can bedetermined by the machine learning component 106 based on the inputdata.

The GUI component 107 can generate a graphical user interface thatpresents the 3D model. The graphical user interface can be a graphicaluser interface for a display device. The GUI component 107 can alsopresent the physics modeling data via the graphical user interface. Forinstance, the GUI component 107 can render the physics modeling data onthe 3D model. In an aspect, the GUI component 107 can present one ormore one or more mechanical components associated with the library ofdata elements 114. The one or more one or more mechanical componentsassociated with the library of data elements 114 can be presented as the3D model based on processing performed by the modeling component 104and/or the machine learning component 106. In an embodiment, the GUIcomponent 107 can include the 3D design component 108 to provide a 3Ddesign environment associated with the 3D model. For instance, the 3Ddesign component 108 can provide a 3D design environment associated witha mechanical element and/or a 3D model generated by the modelingcomponent 104. The 3D design environment can be a single fluid systemdesign tool. For example, the 3D design environment can be a tool thatprovides functionality of numerous tools with respect to fluid systemsto provide multi-disciplinary type analyses. In one example, the 3Ddesign environment can provide a flow integrated design environment, aheat transfer design environment and/or a combustion design environment.In another example, the 3D design environment can be a combustion designenvironment solver associated with the 3D design component 108. The 3Ddesign environment associated with the 3D design component 108 can beemployed to apply one or more numerical schemes to create predictionsfor machine simulated conditions. Prediction can be displayed andanalyzed on a visual representation of actual hardware using apost-processing module of a graphical user interface. In an aspect, the3D design environment associated with the 3D design component 108 cangenerate simulation predictions by conserving governing conservationequations for mass, momentum, energy, angular momentum, and/or speciesutilizing numerical analysis schemes. In certain embodiments, the fluidmodel tool component 102 can be employed as a service. For example, the3D model associated with the fluid model tool component 102 can be agenerated computational model employed by the 3D design environment.

In an embodiment, the 3D design environment can render physics modelingdata of the device based on the input data and the one or morecharacteristics of the mechanical device on the 3D model. The physicsmodeling data can be indicative of a visual representation of the fluidflow, the thermal characteristics, the combustion characteristics and/orthe physics behavior with respect to the 3D model. The physics modelingdata can also be rendered on the 3D model as one or more dynamic visualelements. In an aspect, the 3D design component 108 can alter visualcharacteristics (e.g., color, size, hues, shading, etc.) of at least aportion of the physics modeling data based on the fluid flow, thethermal characteristics, the combustion characteristics and/or thephysics behavior. For example, different degrees of fluid flow throughthe 3D model can be presented as different visual characteristics (e.g.,colors, sizes, hues or shades, etc.), different degrees of thermalcharacteristics with respect to the 3D model can be presented asdifferent visual characteristics (e.g., colors, sizes, hues or shades,etc.), different degrees of combustion characteristics with respect tothe 3D model can be presented as different visual characteristics (e.g.,colors, sizes, hues or shades, etc.), different degrees of physicsbehavior with respect to the 3D model can be presented as differentvisual characteristics (e.g., colors, sizes, hues or shades, etc.), etc.In another aspect, the 3D design environment for the 3D model can allowa user to zoom into or out from the 3D model associated with the physicsmodeling data, rotate a view for the 3D model associated with thephysics modeling data, etc. As such, a user can view, analyze and/orinteract with the 3D model associated with the physics modeling data tofacilitate determination of impact of a fluid flow, thermalcharacteristics, combustion characteristics and/or physics behavior withrespect to a design of the device associated with the 3D model.

In another embodiment, the GUI component 107 can additionally oralternatively include the probabilistic simulation component 109 toprovide a probabilistic simulation environment associated withoptimization of the 3D model. For instance, the probabilistic simulationenvironment provided by the probabilistic simulation component 109 canfacilitate optimization of prototype and/or design of a device within a3D modeling environment. In an aspect, the probabilistic simulationenvironment provided by the probabilistic simulation component 109 canrender a modified version of the physics modeling data on the 3D modelbased on a machine learning process associated with optimization of the3D model. For example, the modified version of the physics modeling datacan be indicative of a visual representation of optimized fluid flow,optimized thermal characteristics, optimized combustion characteristicsand/or optimized physics behavior with respect to the 3D model. Themodified version of the physics modeling data can also be rendered onthe 3D model as one or more dynamic visual elements. In an aspect, theprobabilistic simulation component 109 can alter visual characteristics(e.g., color, size, hues, shading, etc.) of at least a portion of thephysics modeling data based on the optimized fluid flow, the optimizedthermal characteristics, the optimized combustion characteristics and/orthe optimized physics behavior. For example, different degrees of fluidflow through an optimized 3D model can be presented as different visualcharacteristics (e.g., colors, sizes, hues or shades, etc.), differentdegrees of thermal characteristics with respect to the optimized 3Dmodel can be presented as different visual characteristics (e.g.,colors, sizes, hues or shades, etc.), different degrees of combustioncharacteristics with respect to the optimized 3D model can be presentedas different visual characteristics (e.g., colors, sizes, hues orshades, etc.), different degrees of physics behavior with respect to theoptimized 3D model can be presented as different visual characteristics(e.g., colors, sizes, hues or shades, etc.), etc. In another aspect, theprobabilistic simulation environment for the optimized 3D model canallow a user to zoom into or out from the optimized 3D model associatedwith the physics modeling data, rotate a view for the optimized 3D modelassociated with the physics modeling data, etc. As such, a user canview, analyze and/or interact with the optimized 3D model associatedwith the modified version of the physics modeling data to facilitatedetermination of optimized fluid flow, optimized thermalcharacteristics, optimized combustion characteristics and/or optimizedphysics behavior with respect to an optimized design of the deviceassociated with the optimized 3D model.

The probabilistic simulation environment provided by the probabilisticsimulation component 109 can also allow input data to be received viathe graphical user interface to facilitate optimization of prototypeand/or design of a device within a 3D modeling environment. Forinstance, a new set of parameters for a fluid capable of flowing throughthe 3D model can be received via the graphical user interface tofacilitate optimization, a new set of parameters for a thermal energycapable of flowing through the 3D model can be received via thegraphical user interface to facilitate optimization, a new set ofparameters for a combustion chemical reaction capable of flowing throughthe 3D model can be received via the graphical user interface tofacilitate optimization, a new set of parameters for electricity flowingthrough the 3D model can be received via the graphical user interface tofacilitate optimization, and/or another new set of parameters for inputprovided to the 3D model can be received via the graphical userinterface to facilitate optimization. In an aspect, the probabilisticsimulation environment and the 3D design environment can be provided ina corresponding graphical user interface. For instance, theprobabilistic simulation environment and the 3D design environment canbe an integrated system for prototype and/or design of a device within asingle 3D modeling environment. As such, design and optimization of adevice can be provided in a single graphical user interface. In certainembodiments, optimization of prototype and/or design of a device canadditionally or alternatively be visualized as a visual graph or avisual plot via the probabilistic simulation environment. For example,in addition to or rather than rendering the modified version of thephysics modeling data on the 3D model, the modified version of thephysics modeling data and/or other characteristics of a deviceassociated with the 3D model can be presented as a visual graph or avisual plot via the probabilistic simulation environment.

Referring now to FIG. 2, there is illustrated an example system 200 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 200 can include the fluid model tool component 102 and/or thelibrary of data elements 114. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the GUI component 107, the memory 110 and/or the processor 112. The GUIcomponent 107 can include the 3D design component 108 and/or theprobabilistic simulation component 109. In the embodiment shown in FIG.2, the modeling component 104 can include a selection component 202. Inan embodiment, the graphical user interface provided by the GUIcomponent 107 can present a set of components (e.g., a set of mechanicalcomponents and/or a set of electrical components) associated with thelibrary of data elements 114. For example, the graphical user interfaceprovided by the GUI component 107 can present a set of componentsassociated with the library of data elements 114 based on a set ofphysical characteristics associated with the set of components. Physicalcharacteristics can include, for example, material (e.g., metal, alloy,etc.), shape, size, dimension and/or other physical characteristics.Additionally or alternatively, the graphical user interface provided bythe GUI component 107 can present a set of components associated withthe library of data elements 114 based on a set of thermalcharacteristics associated with the set of components. Thermalcharacteristics can include, for example, thermal conductivity, thermalconductance, thermal U-factor, thermal mass and/or other thermalcharacteristics. In an embodiment, the GUI component 107 can presentinformation for the set of components (e.g., information associated withthe set of physical characteristics and/or the set of thermalcharacteristics for the set of components) on the graphical userinterface generated by the GUI component 107. The set of componentsand/or information for the set of components can be presented, forexample, as a list of one or more components via the graphical userinterface. The set of components can also be presented withcorresponding textual data and/or visual data to facilitateidentification and/or selection of one or more components from the setof components.

As such, a user can employ the graphical user interface generated by theGUI component 107 to select one or more components from the set ofcomponents. The selection component 202 can receive a selection of oneor more components from the set of components presented via thegraphical user interface. For example, the selection component 202 canreceive a selection of one or more mechanical components and/or one ormore electrical components presented via the graphical user interface.In an non-limiting example, a user can employ a display device thatpresents the graphical user interface to select the one or morecomponents from the library of data elements 114 accessed via thegraphical user interface. The selection of one or more components fromthe set of components can be received as selection data. The selectioncomponent 202 can also modify a 3D model (e.g., a 3D model generated bythe modeling component 104) based on the selection of the one or morecomponents to generate a modified version of the 3D model. For instance,the selection component 202 can add the one or more components to a 3Dmodel previously generated by the modeling component 104. The selectioncomponent 202 can also receive location data indicative of a locationfor the one or more components with respect to the 3D model previouslygenerated by the modeling component 104. For example, the selection ofthe one or more components can include a coordinate system locationand/or an orientation for the one or more components to facilitategeneration of the modified version of the 3D model. In an embodiment,the machine learning component 106 can perform a machine learningprocess based on the modified version of the 3D model that includes theone or more components selected via the graphical user interface (e.g.,the one or more components selected based on the set of physicalcharacteristics and/or the set of thermal characteristics).

Referring now to FIG. 3, there is illustrated an example system 300 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 300 can include the fluid model tool component 102 and/or thelibrary of data elements 114. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the GUI component 107, the memory 110 and/or the processor 112. The GUIcomponent 107 can include the 3D design component 108 and/or theprobabilistic simulation component 109. In the embodiment shown in FIG.3, the machine learning component 106 can include an optimizationcomponent 302. In certain embodiments, the modeling component 104 canalso include the selection component 202. The optimization component 302can perform a machine learning process associated with optimization of a3D model. In an aspect, a machine learning process performed by theoptimization component 302 can generate a modified version of physicsmodeling data for a 3D model. For example, the modified version of thephysics modeling data can be optimized physics modeling data tofacilitate generation of an optimized 3D model by the optimizationcomponent 302. In an embodiment, the optimization component 302 canperform a machine learning process to generate a modified version ofphysics modeling data based on a Latin hypercube sampling process thatmodifies one or more values of the physics modeling data. The Latinhypercube sampling process can be a process of randomly sampling valuesfor the modified version of physics modeling data based on two or moredata sets of data for the physics modeling data. For instance, with theLatin hypercube sampling process, a set of potential data values for themodified version of physics modeling data can be sampled based on aneven sampling technique. Furthermore, two or more data set from the setof potential data values can be randomly combined and employed as one ormore data values for the modified version of physics modeling data. Inanother embodiment, the optimization component 302 can perform a machinelearning process to generate a modified version of physics modeling databased on a Monte Carlo sampling process that modifies one or more valuesof the physics modeling data. The Monte Carlo sampling process canperform repeated random sampling to determine values for the modifiedversion of physics modeling data. For instance, the Monte Carlo samplingprocess can employ random values from a probability distributionfunction to determine one or more data values for the modified versionof physics modeling data. In certain embodiments, the optimizationcomponent 302 can employ the Latin hypercube sampling process incombination with the Monte Carlo sampling process to determine one ormore data values for the modified version of physics modeling data. Forexample, the Latin hypercube sampling process can be incorporated intothe Monte Carlo sampling process to determine one or more data valuesfor the modified version of physics modeling data.

Referring now to FIG. 4, there is illustrated an example system 400 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 400 can include the fluid model tool component 102, thelibrary of data elements 114 and a user display device 402. The userdisplay device 402 can be in communication with the fluid model toolcomponent 102 via a network 404. The network 404 can be a wired networkand/or a wireless network. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the GUI component 107, the memory 110 and/or the processor 112. Incertain embodiments, the GUI component 107 can include 3D designcomponent 108 and/or the probabilistic simulation component 109.Furthermore, in certain embodiments, the modeling component 104 caninclude the selection component 202 and/or the machine learningcomponent 106 can include the optimization component 302. The userdisplay device 402 can display a 3D model and/or a 3D design environmentgenerated by the fluid model tool component 102. For example, a 3D modelassociated with physics modeling data can be rendered on a graphicaluser interface associated with a display of the user display device 402.The user display device 402 can be a device with a display such as, butnot limited to, a computing device, a computer, a desktop computer, alaptop computer, a monitor device, a smart device, a smart phone, amobile device, a handheld device, a tablet, a portable computing deviceor another type of user device associated with a display.

Referring now to FIG. 5, there is illustrated an example system 500 thatfacilitates optimization of prototype and machine design within a 3Dfluid modeling environment, according to an aspect of the subjectdisclosure. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

The system 500 can include a 3D model 502. The 3D model 502 can begenerated by the modeling component 104. In an aspect, the 3D model 502can be generated based on the library of data elements 114 and/or aselection of one or more components of the 3D model 502 via a graphicaluser interface generated by the GUI component 107. In another aspect,the 3D model 502 can be associated with a device (e.g., a mechanicaldevice and/or an electrical device). The system 500 can also include amachine learning process 504. The machine learning process 504 can beperformed by the machine learning component 106. Furthermore, themachine learning process 504 can be a machine learning processassociated with the 3D model 502. In an aspect, the machine learningcomponent 106 can perform the machine learning process 504 based on 3Dmodel data (e.g., 3D MODEL DATA shown in FIG. 6). The machine learningcomponent 106 can also perform the machine learning process 504 based onlearning and/or generated inferences associated with the 3D model 502.

The system 500 can also include an optimization probabilistic process505 associated with optimization of the 3D model 502. The optimizationprobabilistic process 505 can be a machine learning process performed bythe optimization component 302. For example, the machine learningprocess 504 can be a first machine learning process performed by themachine learning component 106 and the optimization probabilisticprocess 505 can be a second machine learning process performed by theoptimization component 302. In an aspect, the optimization probabilisticprocess 505 can perform the machine learning process based on the 3Dmodel data and/or input data received via the graphical user interfacegenerated by the GUI component 107 to facilitate optimization ofprototype and/or design of the 3D model 502. Input data received via thegraphical user interface can include, for example, a new set ofparameters for a fluid capable of flowing through the 3D model 502, anew set of parameters for a thermal energy capable of flowing throughthe 3D model 502, a new set of parameters for a combustion chemicalreaction capable of flowing through the 3D model 502, a new set ofparameters for electricity flowing through the 3D model 502, and/oranother new set of parameters for input provided to the 3D model 502.The optimization component 302 can also perform the optimizationprobabilistic process 505 based on learning and/or generated inferencesassociated with the 3D model 502.

Additionally, the system 500 can include a physics 3D model 506. Thephysics 3D model 506 can be associated with the 3D model 502. Thephysics 3D model 506 can also include physics modeling data (e.g.,PHYSICS MODELING DATA shown in FIG. 5) generated by the machine learningprocess 504. The physics modeling data can be indicative of informationassociated with fluid dynamics, thermal dynamic and/or combustiondynamics For instance, the physics modeling data can be rendered on thephysics 3D model 506 to represent fluid flow, thermal characteristics,combustion characteristics and/or physics behavior for a deviceassociated with the physics 3D model 506. In one example, the physicsmodeling data can simulate physical phenomena such as, but not limitedto, compressible fluid flow, incompressible fluid flow, buoyancy drivenflow, rotating cavity system flow, conduction heat transfer, convectionheat transfer, radiation heat transfer, combustionequilibrium-chemistry, species transport, and/or other physics behavior.

Moreover, the system 500 can include an optimized physics 3D model 507.The optimized physics 3D model 507 can be a modified version of thephysics 3D model 506. The optimized physics 3D model 507 can include amodified version of the physics modeling data (e.g., MODIFIED VERSION OFPHYSICS MODELING DATA shown in FIG. 5) generated by the machine learningprocess 504. The modified version of the physics modeling data can beindicative of optimized information associated with fluid dynamics,thermal dynamic and/or combustion dynamics For instance, the modifiedversion of the physics modeling data can be rendered on the optimizedphysics 3D model 507 to represent optimized fluid flow, optimizedthermal characteristics, optimized combustion characteristics and/oroptimized physics behavior for a device associated with the 3D model502. In one example, the modified version of the physics modeling datacan simulate optimized physical phenomena such as, but not limited to,optimized compressible fluid flow, optimized incompressible fluid flow,optimized buoyancy driven flow, optimized rotating cavity system flow,optimized conduction heat transfer, optimized convection heat transfer,optimized radiation heat transfer, optimized combustionequilibrium-chemistry, optimized species transport, and/or otheroptimized physics behavior.

FIG. 6 illustrates an example system 600, in accordance with variousaspects and implementations described herein. The system 600 can includea graphical user interface 602. The graphical user interface 602 caninclude a model design section 604 and a model probabilistic simulationsection 606. The model design section 604 can be associated with themodeling component 104 and/or the 3D design component 108. The modeldesign section 604 can also facilitate design of a 3D model generated bythe modeling component 104 such as, for example, the 3D model 502. Incertain embodiments, the model design section 604 can present a set ofcomponents to facilitate selection of one or more components from theset of components for a 3D model. In one example, the model designsection 604 can be associated with a 3D model design environment. In anaspect, the model design section 604 can provide visualization of a 3Dmodel generated by the modeling component 104 such as, for example, the3D model 502. For example, the model design section 604 can provide a 3Ddesign environment that renders physics modeling data on a 3D model. Themodel probabilistic simulation section 606 can be associated with theprobabilistic simulation component 109. The model probabilisticsimulation section 606 can also facilitate design of an optimized 3Dmodel. In certain embodiments, the model probabilistic simulationsection 606 can present a set of components based on a set of physicalcharacteristics and/or a set of thermal characteristics to facilitateselection of one or more components from the set of components for a 3Dmodel. In one example, the model probabilistic simulation section 606can be associated with a probabilistic simulation environment. In anaspect, the model probabilistic simulation section 606 can providevisualization of an optimized 3D model generated by the optimizationcomponent 302 such as, for example, the optimized physics 3D model 507.For example, the model probabilistic simulation section 606 can providea probabilistic simulation environment that renders a modified versionof physics modeling data on an optimized physics 3D model based on amachine learning process associated with the optimization of the 3Dmodel.

FIG. 7 illustrates an example 3D model 700, in accordance with variousaspects and implementations described herein. The 3D model 700 can, forexample, correspond to the physics 3D model 506, the optimized physics3D model 507 and/or a 3D model generated by the fluid model toolcomponent 102. The 3D model 700 can illustrate fluid dynamics, thermaldynamic and/or combustion dynamics with respect to a design of a device.For example, the 3D model 700 can be a 3D model where physics modelingdata associated with fluid dynamics, thermal dynamic and/or combustiondynamics is rendered on a device. In an aspect, the 3D model 700 caninclude a device portion 702 of the 3D model 700 and physics modelingdata 704 that is rendered on the device portion 702. Visualcharacteristics (e.g., a color, a size, a hues, shading, etc.) of thephysics modeling data 704 can be dynamic based on a value of the physicsmodeling data 704. For instance, a first portion of the physics modelingdata 704 associated with first physics modeling information can comprisea first visual characteristics and a second portion of the physicsmodeling data 704 associated with second physics modeling informationcan comprise a second visual characteristic. In an embodiment, thephysics modeling data 704 can be determined by the machine learningcomponent 106. In one example, the physics modeling data 704 can beassociated with a set of control volumes and/or a flow network relatedto fluid dynamics, thermal dynamic and/or combustion dynamics In anembodiment, a 3D design environment associated with the 3D model 700 caninclude a heat bar 706. The heat bar 706 can include a set of colorsthat correspond to different values for the physics modeling data 704.For example, a first color (e.g., a color red) in the heat bar 706 cancorrespond to a first value for the physics modeling data 704 and asecond color (e.g., a color blue) in the heat bar 706 can correspond toa second value for the physics modeling data 704. In another embodiment,a 3D design environment associated with the 3D model 700 can include aside bar 708. The side bar 708 can include information to facilitategeneration of the 3D model 700 and/or the physics modeling data 704. Forexample, the side bar 708 can facilitate selection of one or moresub-components (e.g., flow elements, tubes, orifices, bends valves,junctions, fans, compressors, another other component, etc.) that formthe device portion 702 of the 3D model 700. In another example, the sidebar 708 can facilitate selection of a type of physics modeling data(e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.)provided by the physics modeling data 704.

FIG. 8 illustrates an example 3D model 800, in accordance with variousaspects and implementations described herein. The 3D model 800 can, forexample, correspond to the physics 3D model 506, the optimized physics3D model 507 and/or a 3D model generated by the fluid model toolcomponent 102. The 3D model 800 can illustrate fluid dynamics, thermaldynamic and/or combustion dynamics with respect to a design of a device.For example, the 3D model 800 can be a 3D model where physics modelingdata associated with fluid dynamics, thermal dynamic and/or combustiondynamics is rendered on a device. In an aspect, the 3D model 800 caninclude a device portion 802 of the 3D model 800 and physics modelingdata 804 that is rendered on the device portion 802. Visualcharacteristics (e.g., a color, a size, a hues, shading, etc.) of thephysics modeling data 804 can be dynamic based on a value of the physicsmodeling data 804. For instance, a first portion of the physics modelingdata 804 associated with first physics modeling information can comprisea first visual characteristics and a second portion of the physicsmodeling data 804 associated with second physics modeling informationcan comprise a second visual characteristic. In an embodiment, thephysics modeling data 804 can be determined by the machine learningcomponent 106. In one example, the physics modeling data 804 can beassociated with a set of control volumes and/or a flow network relatedto fluid dynamics, thermal dynamic and/or combustion dynamics In anembodiment, a 3D design environment associated with the 3D model 800 caninclude a heat bar 806. The heat bar 806 can include a set of colorsthat correspond to different values for the physics modeling data 804.For example, a first color (e.g., a color red) in the heat bar 806 cancorrespond to a first value for the physics modeling data 804 and asecond color (e.g., a color blue) in the heat bar 806 can correspond toa second value for the physics modeling data 804. In another embodiment,a 3D design environment associated with the 3D model 800 can include aside bar 808. The side bar 808 can include information to facilitategeneration of the 3D model 800 and/or the physics modeling data 804. Forexample, the side bar 808 can facilitate selection of one or moresub-components (e.g., flow elements, tubes, orifices, bends valves,junctions, fans, compressors, another other component, etc.) that formthe device portion 802 of the 3D model 800. In another example, the sidebar 808 can facilitate selection of a type of physics modeling data(e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.)provided by the physics modeling data 804.

FIG. 9 illustrates an example 3D model 900, in accordance with variousaspects and implementations described herein. The 3D model 900 can, forexample, correspond to the physics 3D model 506, the optimized physics3D model 507 and/or a 3D model generated by the fluid model toolcomponent 102. The 3D model 900 can illustrate fluid dynamics, thermaldynamic and/or combustion dynamics with respect to a design of a device.For example, the 3D model 900 can be a 3D model where physics modelingdata associated with fluid dynamics, thermal dynamic and/or combustiondynamics is rendered on a device. In an aspect, the 3D model 900 caninclude a device portion 902 of the 3D model 900 and physics modelingdata 904 that is rendered on the device portion 902. Visualcharacteristics (e.g., a color, a size, a hues, shading, etc.) of thephysics modeling data 904 can be dynamic based on a value of the physicsmodeling data 904. For instance, a first portion of the physics modelingdata 904 associated with first physics modeling information can comprisea first visual characteristics and a second portion of the physicsmodeling data 904 associated with second physics modeling informationcan comprise a second visual characteristic. In an embodiment, thephysics modeling data 904 can be determined by the machine learningcomponent 106. In one example, the physics modeling data 904 can beassociated with a set of control volumes and/or a flow network relatedto fluid dynamics, thermal dynamic and/or combustion dynamics In anembodiment, a 3D design environment associated with the 3D model 900 caninclude a heat bar 906. The heat bar 906 can include a set of colorsthat correspond to different values for the physics modeling data 904.For example, a first color (e.g., a color red) in the heat bar 906 cancorrespond to a first value for the physics modeling data 904 and asecond color (e.g., a color blue) in the heat bar 906 can correspond toa second value for the physics modeling data 904. In another embodiment,a 3D design environment associated with the 3D model 900 can include aside bar 908. The side bar 908 can include information to facilitategeneration of the 3D model 900 and/or the physics modeling data 904. Forexample, the side bar 908 can facilitate selection of one or moresub-components (e.g., flow elements, tubes, orifices, bends valves,junctions, fans, compressors, another other component, etc.) that formthe device portion 902 of the 3D model 900. In another example, the sidebar 908 can facilitate selection of a type of physics modeling data(e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.)provided by the physics modeling data 904.

FIG. 10 illustrates an example 3D model 1000, in accordance with variousaspects and implementations described herein. The 3D model 1000 can, forexample, correspond to the physics 3D model 506, the optimized physics3D model 507 and/or a 3D model generated by the fluid model toolcomponent 102. The 3D model 1000 can illustrate fluid dynamics, thermaldynamic and/or combustion dynamics with respect to a design of a device.For example, the 3D model 1000 can be a 3D model where physics modelingdata associated with fluid dynamics, thermal dynamic and/or combustiondynamics is rendered on a device. In an aspect, the 3D model 1000 caninclude a device portion 1002 of the 3D model 900. The 3D model 1000 canalso include first physics modeling data 1004 a, second physics modelingdata 1004 b and third physics modeling data 1004 c that are rendered onthe device portion 1002. Visual characteristics (e.g., a color, a size,a hues, shading, etc.) of the first physics modeling data 1004 a, thesecond physics modeling data 1004 b and the third physics modeling data1004 c can be dynamic based on a value of the physics modeling data1004. For instance, the first physics modeling data 1004 a can comprisea first visual characteristic (e.g., a yellow color) associated with afirst physics modeling data value, the second physics modeling data 1004b can comprise a second visual characteristic (e.g., a green color)associated with a second physics modeling data value, and the thirdphysics modeling data 1004 c can comprise a third visual characteristic(e.g., a blue color) associated with a third physics modeling datavalue. In an embodiment, the first physics modeling data 1004 a, thesecond physics modeling data 1004 b and the third physics modeling data1004 c can be determined by the machine learning component 106. In oneexample, the first physics modeling data 1004 a, the second physicsmodeling data 1004 b and the third physics modeling data 1004 c can beassociated with a set of control volumes and/or a flow network relatedto fluid dynamics, thermal dynamic and/or combustion dynamics. In anembodiment, a 3D design environment associated with the 3D model 1000can include a heat bar 1006. The heat bar 1006 can include a set ofcolors that correspond to different values for the first physicsmodeling data 1004 a, the second physics modeling data 1004b and thethird physics modeling data 1004 c. For example, a first color (e.g., acolor yellow) in the heat bar 1006 can correspond to the first physicsmodeling data value associated with the first visual characteristic forthe first physics modeling data 1004 a, a second color (e.g., a colorgreen) in the heat bar 1006 can correspond to the second physicsmodeling data value associated with the second visual characteristic forthe second physics modeling data 1004 b, and a third color (e.g., acolor blue) in the heat bar 1006 can correspond to the third physicsmodeling data value associated with the third visual characteristic forthe third physics modeling data 1004 c. In another embodiment, a 3Ddesign environment associated with the 3D model 1000 can include a sidebar 1008. The side bar 1008 can include information to facilitategeneration of the 3D model 1000, the first physics modeling data 1004 a,the second physics modeling data 1004 b and/or the third physicsmodeling data 1004 c. For example, the side bar 1008 can facilitateselection of one or more sub-components (e.g., flow elements, tubes,orifices, bends valves, junctions, fans, compressors, another othercomponent, etc.) that form the device portion 1002 of the 3D model 1000.In another example, the side bar 1008 can facilitate selection of a typeof physics modeling data (e.g., flow dynamics, thermal dynamics,combustion dynamics, etc.) provided by the first physics modeling data1004 a, the second physics modeling data 1004 b and the third physicsmodeling data 1004 c.

The aforementioned systems and/or devices have been described withrespect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component providing aggregatefunctionality. The components may also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

FIGS. 11-12 illustrate methodologies and/or flow diagrams in accordancewith the disclosed subject matter. For simplicity of explanation, themethodologies are depicted and described as a series of acts. It is tobe understood and appreciated that the subject innovation is not limitedby the acts illustrated and/or by the order of acts, for example actscan occur in various orders and/or concurrently, and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be required to implement the methodologies in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methodologies could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be further appreciated that themethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device or storagemedia.

Referring to FIG. 11, there illustrated is a methodology 1100 forproviding interdisciplinary fluid modeling, according to an aspect ofthe subject innovation. As an example, the methodology 1100 can beutilized in various applications, such as, but not limited to, modelingsystems, aviation systems, power systems, distributed power systems,energy management systems, thermal management systems, transportationsystems, oil and gas systems, mechanical systems, machine systems,device systems, cloud-based systems, heating systems, HVAC systems,medical systems, automobile systems, aircraft systems, water craftsystems, water filtration systems, cooling systems, pump systems, enginesystems, diagnostics systems, prognostics systems, machine designsystems, medical device systems, medical imaging systems, medicalmodeling systems, simulation systems, enterprise systems, enterpriseimaging solution systems, advanced diagnostic tool systems, imagemanagement platform systems, artificial intelligence systems, machinelearning systems, neural network systems, etc. At 1102, a 3D model of amechanical device is generated (e.g., by modeling component 104) basedon a library of stored data elements. The library of data elements 114can include a set of data elements for mechanical components of themechanical device and/or electrical components of the mechanical device.The set of data elements can include, for example, geometry data and/ortexture data to facilitate generation of the 3D model. In an embodiment,the generating the 3D model can include integrating a first 3D modelassociated with a first mechanical device and a second 3D modelassociated with a second mechanical device.

At 1104, a first machine learning process associated with the 3D modelis performed (e.g., by machine learning component 106) to predict one ormore characteristics of the mechanical device. The one or morecharacteristics can include, for example, fluid flow, thermalcharacteristics, combustion characteristics and/or physics behavior. Forinstance, the first machine learning process can perform learning and/orcan generate inferences with respect to fluid flow, thermalcharacteristics, combustion characteristics and/or physics behaviorassociated with the 3D model. In an aspect, the first machine learningprocess can also be performed based on input data. The input data caninclude fluid data, electrical data and/or chemical data associated withan input provided to a device associated with the 3D model. The physicsbehavior can be indicative of behavior related to fluid dynamics,thermal dynamics and/or combustion dynamics throughout the deviceassociated with the 3D model in response to the input data.

At 1106, physics modeling data of the mechanical device is generated(e.g., by machine learning component 106) based on the one or morecharacteristics of the mechanical device. The physics modeling data canbe indicative of a visual representation of the fluid flow, the thermalcharacteristics, the combustion characteristics and/or the physicsbehavior with respect to the 3D model.

At 1108, it is determined (e.g., by machine learning component 106)whether the first machine learning process has generated new output. Ifyes, the methodology 1100 returns to 1106 to update the physics modelingdata based on the new output. If no, the methodology 1100 proceeds to1110.

At 1110, a graphical user interface that presents a 3D designenvironment associated with the 3D model is generated (e.g., by GUIcomponent 107), including rendering the physics modeling data on the 3Dmodel via the 3D design environment. In an aspect, the physics modelingdata can be rendered on the 3D model as dynamic visual elements.

At 1112, a modified version of the physics modeling data of themechanical device is generated (e.g., by optimization component 302)based on a second machine learning process associated with optimizationof the 3D model. The modified version of the physics modeling data canbe indicative of a visual representation of optimized fluid flow,optimized thermal characteristics, optimized combustion characteristicsand/or optimized physics behavior with respect to the 3D model.

At 1114, it is determined (e.g., by machine learning component 106)whether the second machine learning process has generated new output. Ifyes, the methodology 1100 returns to 1112 to update the modified versionof the physics modeling data based on the new output. If no, themethodology 1100 proceeds to 1116.

At 1116, the graphical user interface that presents is updated (e.g., byGUI component 107) to present a probabilistic simulation environmentassociated with optimization of the 3D model, including rendering themodified version of the physics modeling data on the 3D model via theprobabilistic simulation environment.

In an embodiment, the methodology 1100 can include displayinginformation associated with a set of components included in the libraryof stored data elements based on a set of physical characteristicsassociated with the set of components. The methodology 1100 can alsoinclude, in certain embodiments, generating a modified version of the 3Dmodel based on a selection of one or more components associated with theset of physical characteristics. Additionally or alternatively, themethodology 1100 can include displaying information associated with aset of components included in the library of stored data elements basedon a set of thermal characteristics associated with the set ofcomponents. The methodology 1100 can also include, in certainembodiments generating a modified version of the 3D model based on aselection of one or more components associated with the set of thermalcharacteristics. In another embodiment, the methodology 1100 canperforming the second machine learning process based on a Latinhypercube sampling technique. Additionally or alternatively, themethodology 1100 can performing the second machine learning processbased on a Monte Carlo sampling technique.

Referring to FIG. 12, there illustrated is a methodology 1200 forproviding interdisciplinary fluid modeling, according to an aspect ofthe subject innovation. As an example, the methodology 1200 can beutilized in various applications, such as, but not limited to, modelingsystems, aviation systems, power systems, distributed power systems,energy management systems, thermal management systems, transportationsystems, oil and gas systems, mechanical systems, machine systems,device systems, cloud-based systems, heating systems, HVAC systems,medical systems, automobile systems, aircraft systems, water craftsystems, water filtration systems, cooling systems, pump systems, enginesystems, diagnostics systems, prognostics systems, machine designsystems, medical device systems, medical imaging systems, medicalmodeling systems, simulation systems, enterprise systems, enterpriseimaging solution systems, advanced diagnostic tool systems, imagemanagement platform systems, artificial intelligence systems, machinelearning systems, neural network systems, etc. At 1202, input dataindicative of input received via a graphical user interface is received(e.g., by GUI component 107). The input data can include fluid data,electrical data and/or chemical data associated with an input providedto a device associated with the 3D model. For example, an optimized setof parameters for a fluid capable of flowing through the 3D model can bereceived via the graphical user interface to facilitate optimization, anoptimized set of parameters for a thermal energy capable of flowingthrough the 3D model can be received via the graphical user interface tofacilitate optimization, an optimized set of parameters for a combustionchemical reaction capable of flowing through the 3D model can bereceived via the graphical user interface to facilitate optimization, anoptimized set of parameters for electricity flowing through the 3D modelcan be received via the graphical user interface to facilitateoptimization, and/or another optimized set of parameters for inputprovided to the 3D model can be received via the graphical userinterface to facilitate optimization.

At 1204, a 3D model of a mechanical device is updated (e.g., by modelingcomponent 104) based on the input data and/or a library of stored dataelements. The library of data elements 114 can include a set of dataelements for mechanical components of the mechanical device and/orelectrical components of the mechanical device. The set of data elementscan include, for example, geometry data and/or texture data tofacilitate generation of the 3D model.

At 1206, optimized physics modeling data of the mechanical device isgenerated (e.g., by optimization component 302) by performing a machinelearning process associated with optimization of the 3D model of themechanical device. The optimized physics modeling data can be associatedwith one or more optimized characteristics with respect to themechanical device such as, for example, optimized fluid flow, optimizedthermal characteristics, optimized combustion characteristics and/oroptimized physics behavior. For instance, the machine learning processcan perform learning and/or can generate inferences with respect tooptimized fluid flow, optimized thermal characteristics, optimizedcombustion characteristics and/or optimized physics behavior associatedwith the 3D model. The optimized physics modeling data can be indicativeof a visual representation of the optimized fluid flow, the optimizedthermal characteristics, the optimized combustion characteristics and/orthe optimized physics behavior with respect to the updated 3D model. Theoptimized physics behavior can be indicative of behavior related tooptimized fluid dynamics, optimized thermal dynamics and/or optimizedcombustion dynamics throughout the device associated with the 3D modelin response to at least a portion of the input data.

At 1208, it is determined whether the machine learning process hasgenerated new output. If yes, the methodology 1200 returns to 1206 toupdate the optimized physics modeling data based on the new output. Ifno, the methodology 1200 proceeds to 1210.

At 1210, the 3D model is presented via the graphical user interface(e.g., by GUI component 107) and the optimized physics modeling datarendered on the 3D model (e.g., by GUI component 107). In an aspect, theoptimized physics modeling data can be rendered on the 3D model asdynamic visual elements.

At 1212, it is determined whether an input parameter for the optimizedphysics modeling data has been altered. For example, it can bedetermined wither a new input parameter for the machine learning processis provided via the graphical user interface. If yes, the methodology1200 returns to 1206 to perform a new machine learning process based onthe altered input parameters. If no, the methodology 1200 can end.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 13 and 14 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 13, a suitable environment 1300 for implementingvarious aspects of this disclosure includes a computer 1312. Thecomputer 1312 includes a processing unit 1314, a system memory 1316, anda system bus 1318. The system bus 1318 couples system componentsincluding, but not limited to, the system memory 1316 to the processingunit 1314. The processing unit 1314 can be any of various availableprocessors. Dual microprocessors and other multiprocessor architecturesalso can be employed as the processing unit 1314.

The system bus 1318 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1316 includes volatile memory 1320 and nonvolatilememory 1322. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1312, such as during start-up, is stored in nonvolatile memory 1322. Byway of illustration, and not limitation, nonvolatile memory 1322 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory 1320 includes random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asstatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM(SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM),and Rambus dynamic RAM.

Computer 1312 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 13 illustrates, forexample, a disk storage 1324. Disk storage 1324 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. The disk storage 1324 also can include storage media separatelyor in combination with other storage media including, but not limitedto, an optical disk drive such as a compact disk ROM device (CD-ROM), CDrecordable drive (CD-R Drive), CD rewritable drive (CD-RW Drive) or adigital versatile disk ROM drive (DVD-ROM). To facilitate connection ofthe disk storage devices 1324 to the system bus 1318, a removable ornon-removable interface is typically used, such as interface 1326.

FIG. 13 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1300. Such software includes, for example, an operatingsystem 1328. Operating system 1328, which can be stored on disk storage1324, acts to control and allocate resources of the computer system1312. System applications 1330 take advantage of the management ofresources by operating system 1328 through program modules 1332 andprogram data 1334, e.g., stored either in system memory 1316 or on diskstorage 1324. It is to be appreciated that this disclosure can beimplemented with various operating systems or combinations of operatingsystems.

A user enters commands or information into the computer 1312 throughinput device(s) 1336. Input devices 1336 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1314through the system bus 1318 via interface port(s) 1338. Interfaceport(s) 1338 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1340 usesome of the same type of ports as input device(s) 1336. Thus, forexample, a USB port may be used to provide input to computer 1312, andto output information from computer 1312 to an output device 1340.Output adapter 1342 is provided to illustrate that there are some outputdevices 1340 like monitors, speakers, and printers, among other outputdevices 1340, which require special adapters. The output adapters 1342include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1340and the system bus 1318. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1344.

Computer 1312 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1344. The remote computer(s) 1344 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1312. For purposes of brevity, only a memory storage device 1346 isillustrated with remote computer(s) 1344. Remote computer(s) 1344 islogically connected to computer 1312 through a network interface 1348and then physically connected via communication connection 1350. Networkinterface 1348 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1350 refers to the hardware/softwareemployed to connect the network interface 1348 to the bus 1318. Whilecommunication connection 1350 is shown for illustrative clarity insidecomputer 1312, it can also be external to computer 1312. Thehardware/software necessary for connection to the network interface 1348includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 14 is a schematic block diagram of a sample-computing environment1400 with which the subject matter of this disclosure can interact. Thesystem 1400 includes one or more client(s) 1410. The client(s) 1410 canbe hardware and/or software (e.g., threads, processes, computingdevices). The system 1400 also includes one or more server(s) 1430.Thus, system 1400 can correspond to a two-tier client server model or amulti-tier model (e.g., client, middle tier server, data server),amongst other models. The server(s) 1430 can also be hardware and/orsoftware (e.g., threads, processes, computing devices). The servers 1430can house threads to perform transformations by employing thisdisclosure, for example. One possible communication between a client1410 and a server 1430 may be in the form of a data packet transmittedbetween two or more computer processes.

The system 1400 includes a communication framework 1450 that can beemployed to facilitate communications between the client(s) 1410 and theserver(s) 1430. The client(s) 1410 are operatively connected to one ormore client data store(s) 1420 that can be employed to store informationlocal to the client(s) 1410. Similarly, the server(s) 1430 areoperatively connected to one or more server data store(s) 1440 that canbe employed to store information local to the servers 1430.

It is to be noted that aspects or features of this disclosure can beexploited in substantially any wireless telecommunication or radiotechnology, e.g., Wi-Fi; Bluetooth; Worldwide Interoperability forMicrowave Access (WiMAX); Enhanced General Packet Radio Service(Enhanced GPRS); Third Generation Partnership Project (3GPP) Long TermEvolution (LTE); Third Generation Partnership Project 2 (3GPP2) UltraMobile Broadband (UMB); 3GPP Universal Mobile Telecommunication System(UMTS); High Speed Packet Access (HSPA); High Speed Downlink PacketAccess (HSDPA); High Speed Uplink Packet Access (HSUPA); GSM (GlobalSystem for Mobile Communications) EDGE (Enhanced Data Rates for GSMEvolution) Radio Access Network (GERAN); UMTS Terrestrial Radio AccessNetwork (UTRAN); LTE Advanced (LTE-A); etc. Additionally, some or all ofthe aspects described herein can be exploited in legacytelecommunication technologies, e.g., GSM. In addition, mobile as wellnon-mobile networks (e.g., the Internet, data service network such asinternet protocol television (IPTV), etc.) can exploit aspects orfeatures described herein.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthis disclosure also can or may be implemented in combination with otherprogram modules. Generally, program modules include routines, programs,components, data structures, etc. that perform particular tasks and/orimplement particular abstract data types. Moreover, those skilled in theart will appreciate that the inventive methods may be practiced withother computer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., PDA, phone), microprocessor-based or programmable consumer orindustrial electronics, and the like. The illustrated aspects may alsobe practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. However, some, if not all aspects of thisdisclosure can be practiced on stand-alone computers. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers.

In another example, respective components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor. In such acase, the processor can be internal or external to the apparatus and canexecute at least a part of the software or firmware application. As yetanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,wherein the electronic components can include a processor or other meansto execute software or firmware that confers at least in part thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

As used herein, the terms “example” and/or “exemplary” are utilized tomean serving as an example, instance, or illustration. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

Various aspects or features described herein can be implemented as amethod, apparatus, system, or article of manufacture using standardprogramming or engineering techniques. In addition, various aspects orfeatures disclosed in this disclosure can be realized through programmodules that implement at least one or more of the methods disclosedherein, the program modules being stored in a memory and executed by atleast a processor. Other combinations of hardware and software orhardware and firmware can enable or implement aspects described herein,including a disclosed method(s). The term “article of manufacture” asused herein can encompass a computer program accessible from anycomputer-readable device, carrier, or storage media. For example,computer readable storage media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical discs (e.g., compact disc (CD), digital versatile disc(DVD), blu-ray disc (BD) . . . ), smart cards, and flash memory devices(e.g., card, stick, key drive . . . ), or the like.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor may also beimplemented as a combination of computing processing units.

In this disclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), flashmemory, or nonvolatile random access memory (RAM) (e.g., ferroelectricRAM (FeRAM). Volatile memory can include RAM, which can act as externalcache memory, for example. By way of illustration and not limitation,RAM is available in many forms such as synchronous RAM (SRAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct RambusRAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM(RDRAM). Additionally, the disclosed memory components of systems ormethods herein are intended to include, without being limited toincluding, these and any other suitable types of memory.

It is to be appreciated and understood that components, as describedwith regard to a particular system or method, can include the same orsimilar functionality as respective components (e.g., respectively namedcomponents or similarly named components) as described with regard toother systems or methods disclosed herein.

What has been described above includes examples of systems and methodsthat provide advantages of this disclosure. It is, of course, notpossible to describe every conceivable combination of components ormethods for purposes of describing this disclosure, but one of ordinaryskill in the art may recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A system, comprising: a memory storing computerexecutable components; and a processor configured to execute thefollowing computer executable components stored in the memory: amodeling component that generates a three-dimensional model of amechanical device based on a library of stored data elements; a machinelearning component that predicts one or more characteristics of themechanical device based on a first machine learning process associatedwith the three-dimensional model, and generates physics modeling data ofthe mechanical device based on the one or more characteristics of themechanical device; and a graphical user interface component thatprovides, via a graphical user interface, a three-dimensional designenvironment associated with the three-dimensional model and aprobabilistic simulation environment associated with optimization of thethree-dimensional model, wherein the three-dimensional designenvironment renders the physics modeling data on the three-dimensionalmodel, and wherein the probabilistic simulation environment renders amodified version of the physics modeling data on the three-dimensionalmodel based on a second machine learning process associated with theoptimization of the three-dimensional model.
 2. The system of claim 1,wherein the graphical user interface component presents, via a displaydevice associated with the graphical user interface, a set of componentsassociated with the library of stored data elements based on a set ofphysical characteristics associated with the set of components.
 3. Thesystem of claim 2, wherein the modeling component modifies thethree-dimensional model to generate a modified version of thethree-dimensional model based on a selection of one or more componentsassociated with the set of physical characteristics.
 4. The system ofclaim 3, wherein the machine learning component performs the secondmachine learning process based on the modified version of thethree-dimensional model.
 5. The system of claim 1, wherein the graphicaluser interface component presents, via a display device associated withthe graphical user interface, a set of components associated with thelibrary of stored data elements based on a set of thermalcharacteristics associated with the set of components.
 6. The system ofclaim 5, wherein the modeling component modifies the three-dimensionalmodel to generate a modified version of the three-dimensional modelbased on a selection of one or more components associated with the setof thermal characteristics.
 7. The system of claim 6, wherein themachine learning component performs the second machine learning processbased on the modified version of the three-dimensional model.
 8. Thesystem of claim 1, wherein the machine learning component performs thesecond machine learning process associated with the optimization of thethree-dimensional model, and generates the modified version of thephysics modeling data of based on the second machine learning process.9. The system of claim 1, wherein the machine learning componentperforms the second machine learning process to generate the modifiedversion of the physics modeling data based on a Latin hypercube samplingprocess that modifies one or more values of the physics modeling data.10. The system of claim 1, wherein the machine learning componentperforms the second machine learning process to generate the modifiedversion of the physics modeling data based a Monte Carlo samplingprocess that modifies one or more values of the physics modeling data.11. A method, comprising: generating, by a system comprising aprocessor, a three-dimensional model of a mechanical device based on alibrary of stored data elements; performing, by the system, a firstmachine learning process associated with the three-dimensional model topredict one or more characteristics of the mechanical device;generating, by the system, physics modeling data of the mechanicaldevice based on the one or more characteristics of the mechanicaldevice; and generating, by the system, a graphical user interface thatpresents a three-dimensional design environment associated with thethree-dimensional model and a probabilistic simulation environmentassociated with optimization of the three-dimensional model, comprisingrendering the physics modeling data on the three-dimensional model viathe three-dimensional design environment, and rendering a modifiedversion of the physics modeling data on the three-dimensional model viathe probabilistic simulation environment based on a second machinelearning process associated with the optimization of thethree-dimensional model.
 12. The method of claim 11, further comprising:displaying, by the system, information associated with a set ofcomponents included in the library of stored data elements based on aset of physical characteristics associated with the set of components.13. The method of claim 12, further comprising: generating, by thesystem, a modified version of the three-dimensional model based on aselection of one or more components associated with the set of physicalcharacteristics.
 14. The method of claim 11, further comprising:displaying, by the system, information associated with a set ofcomponents included in the library of stored data elements based on aset of thermal characteristics associated with the set of components.15. The method of claim 14, further comprising: generating, by thesystem, a modified version of the three-dimensional model based on aselection of one or more components associated with the set of thermalcharacteristics.
 16. The method of claim 12, further comprising:performing, by the system, the second machine learning process based ona Latin hypercube sampling technique or a Monte Carlo samplingtechnique.
 17. A computer readable storage device comprisinginstructions that, in response to execution, cause a system comprising aprocessor to perform operations, comprising: generating athree-dimensional model of a mechanical device based on a library ofstored data elements; performing a machine learning process associatedwith the three-dimensional model to predict one or more characteristicsof the mechanical device; determining physics modeling data of themechanical device based on the one or more characteristics of themechanical device; and providing a graphical user interface thatpresents a three-dimensional design environment associated with thethree-dimensional model and a probabilistic simulation environmentassociated with optimization of the three-dimensional model.
 18. Thecomputer readable storage device of claim 17, wherein the operationsfurther comprise: selecting a portion of the mechanical device from thelibrary of stored data elements based on a set of characteristics for aset of components presented via the graphical user interface.
 19. Thecomputer readable storage device of claim 17, wherein the machinelearning process is a first machine learning process, and wherein theoperations further comprise: performing a second machine learningprocess that determines a modified version of the physics modeling databased on a Latin hypercube sampling technique.
 20. The computer readablestorage device of claim 17, wherein the machine learning process is afirst machine learning process, and wherein the operations furthercomprise: performing a second machine learning process that determines amodified version of the physics modeling data based on a Monte Carlosampling technique.